Counties in China play a pivotal role in economic and social development, acting as essential leadership hubs for large and medium-sized cities, contributing to rural revitalization, and facilitating urban-rural integration. Using ArcGIS spatial analysis, this study examines the population distribution spatial and dispersion patterns in Chinese counties based on 40 years of data from the first to the seventh national population census. Results reveal noticeable growth trends and regional disparities in county populations, with an increase in large-population counties and a decrease in small-population ones. Recent population growth concentrates in urban agglomerations, metropolitan areas, and southeastern coastal regions, while reductions occur in the northeastern and Inner Mongolia border areas. Furthermore, the study identifies “high-high” agglomerations around provincial capitals and “low-low” agglomerations in economically underdeveloped western and northeastern border regions. China’s population distribution spatial agglomeration has been increasing, with acceleration toward specific areas. The first through seventh census show rapid growth at low density, followed by growth at medium density, stable growth, and eventually negative growth. This suggests a likely slowdown and potential reversal in China’s future population growth. Additionally, an Auto Regression Integrated Moving Average (ARIMA) model is employed to forecast China’s total population, projecting a decline to 1343.68 million by 2035. The emergence of “population loss counties” in contemporary China underscores the need for a rational understanding of their development status and trends to optimize population development strategies and promote economic and social progress.

This study investigated the impact of three ecological restoration methods: PRS, OLRS, and NRM, on the soil quality and ecological environment of the cold plateau area in northwest Sichuan, China. Over a period of 3 years, these restoration modes were applied, and their effects on soil enzyme activity, organic carbon, and nitrogen were critically analyzed. The results demonstrated that PRS was the most effective restoration method, enhancing soil water content, organic activity, and enzyme content the most, followed by OLRS and NRM, respectively. In the 0~60 cm soil layer, each restoration model was able to improve the vegetation characteristics, and the modification effects were PRS > ORRS > NRM > UMD in descending order. After the restoration, the summer surface temperature decreased from 41.3 °C to 23.1 °C. The average moisture content of the soil increased from 3.11% to 5.86%. The average moisture content of the soil increased from 3.11% to 5.86%, and the bulk density decreased from 1.47 g/m³ to 1.40 g/m³, resulting in a more rational soil structure. This research offers invaluable insights into the nutrient restoration mechanism of sandy lands, emphasizing the significant role of appropriate vegetation restoration measures in ecological restoration. The findings further suggest the potential application of the PRS model for optimal soil repair effects in similar ecological settings.

Biochar is extensively used for ameliorating soil structure. However, the evaluation of soil physical properties influenced by its large-scale use is still unclear. Moreover, research on biochar is mainly based on laboratory tests, lacking medium and long-term field positioning tests. In this study, a local field trial was conducted for 3 years on the Loess Plateau in central Gansu, China. Six levels of biochar treatment were set with application amounts of 0 t·ha-1, 10 t· ha-1, 20 t·ha-1, 30 t·ha-1, 40 t·ha-1, and 50 t·ha-1 (CK, T1, T2, T3, T4, and T5, respectively). The influence of various input levels of biochar on soil porosity, bulk density, aggregate stability, and soil saturated hydraulic conductivity from the 0 cm to 30 cm layer were studied during the three years of wheat field experiments. The experimental study indicates that the increase of biochar addition can significantly (p < 0.05) reduce soil bulk density and increase not only soil saturated hydraulic conductivity but also total soil porosity, content of soil mechanically stable aggregates, and its MWD from the 0 cm to 30 cm soil layer. Among them, T5 treatment is the most significant (p < 0.05). While its content determination of Water Stable aggregates and its MWD only have an obvious (p < 0.05) improvement effect from the 0 cm to 20 cm soil layer, and T5 is the most significant. Therefore, large-scale biochar application is conducive to the improvement of farmland soil physical properties in this region.

Drought is one of the major disasters constraining crop production. The accurate identification of the dominant environmental factors that drive drought stress at different growth stages of maize is essential for developing stage-specific and precise water management strategies, enhancing drought resistance, and ensuring food security. However, a key challenge is quantifying the nonlinear interactions among multiple environmental factors. This study focuses on the rain-fed agricultural region of Northwest China. To address the limited availability of drought event samples in this region and the inadequacy of traditional statistical methods in capturing complex inter-factor relationships, we integrate a small-sample modeling framework based on an improved Conditional Generative Adversarial Network (CGAN) with an attribution framework that employs SHapley Additive exPlanations (SHAP) for interpretability analysis. We incorporate ten environmental factors derived from multi-source remote sensing: temperature (T_max, T_min, T_mean), precipitation (P), evapotranspiration (ET), soil moisture at 0–10 cm (SM_0–10) and at 10–40 cm (SM_10–40), and solar-induced chlorophyll fluorescence (SIF_max, SIF_min, SIF_mean). Sample sets were established for different maize phenological stages. The CGAN model was employed to achieve high-precision estimation of maize drought severity levels, while the SHAP method was used to quantitatively analyze the dominant factors and their contributions at each phenological stage. The results show that the CGAN model achieved coefficients of determination (R²) of 0.963, 0.972, and 0.979 for the seedling, jointing–tasseling, and maturity stages, respectively, demonstrating excellent nonlinear modeling capability under small samples. SHAP analysis reveals a clear dynamic evolution of dominant factors across phenological stages. Evapotranspiration (ET) dominated in the seedling stage, reflecting the primary role of surface water–heat balance, while the jointing–tasseling stage transitioned to a co-dominance of ET, topsoil moisture (SM_0–10), and minimum SIF, indicating intensified crop transpiration and physiological stress under the meteorological drought framework, and the maturity stage shifted to an absolute dominance centered on mean temperature (T_mean), highlighting the critical impact of heat stress. This study provides a data-driven quantitative perspective for understanding maize drought mechanisms and offers a scientific basis for formulating differentiated drought management strategies for different growth stages. Furthermore, it demonstrates the potential of integrating CGAN with SHAP for agricultural remote sensing and drought attribution research in data-scarce regions.